Methane Emission Reduction and Biological Characteristics of Landfill Cover Soil Amended With Hydrophobic Biochar

Co-application of digestate and biochar reduced greenhouse gas emissions in paddy soil through enhanced denitrification and anaerobic methane oxidation

Digestate from food waste (FW) has been identified as a promising nutrient resource for agriculture. However, applying digestate directly to soil often produces considerable greenhouse gas (GHG) emissions. As a soil amendment, biochar has demonstrated potential for mitigating GHG emissions. At present, the effect of biochar on GHG emissions and the associated regulatory mechanisms in paddy soils amended with digestate remains unclear. A 45-day soil incubation was conducted with different nitrogen substitution ratios of urea by digestate, coupled with biochar application: CK (100 % urea), D0U100 (100 % urea + biochar), D50U50 (50 % urea, 50 % digestate + biochar), and D100U0 (100 % digestate + biochar). Results indicated that the co-application of biochar and digestate significantly reduced N2O accumulation by 44.99 %-80.39 % compared to CK, primarily due to a decrease in soil NO3--N content and an increase in soil pH, which together significantly improved the distribution of the nosZ gene involved in denitrification. The increase in the abundance of Conexibacter, Symbiobacterium, Anaerolinea, and Candidatus_Solibacter further contributed to N2O reduction. Furthermore, the co-application led to a 21.68 %-38.15 % reduction in CH4 accumulation compared to CK. Biochar increased the abundance of methanotrophic bacteria, such as Methylococcaceae, Methyloligellaceae, and Methylomirabilaceae. Co-application increased the abundance of nitrate-reducing bacteria Symbiobacterium and Anaerolinea, thereafter facilitating nitrite-dependent anaerobic methane oxidation (AOM) dominated by Methylomirabilaceae. Additionally, sulfate-dependent and Iron(III)-dependent AOM likely further contributed to CH4 reduction. Overall, this study proposed a low-carbon management strategy for FW digestate and GHG emissions mitigation of paddy soil.

Anaerobic oxidation of methane in landfill and adjacent groundwater environments: Occurrence, mechanisms, and potential applications

Landfills remain the predominant means of solid waste management worldwide. Widespread distribution and significant stockpiles of waste in landfills make them a significant source of methane emissions, exacerbating climate change. Anaerobic oxidation of methane (AOM) has been shown to play a critical role in mitigating methane emissions on a global scale. The rich methane and electron acceptor environment in landfills provide the necessary reaction conditions for AOM, making it a potentially low-cost and effective strategy for reducing methane emissions in landfills. However, compared to other anaerobic habitats, research on AOM in landfill environments is scarce, and there is a lack of analysis on the potential application of AOM in different zones of landfills. Therefore, this review summarizes the existing knowledge on AOM and its occurrence in landfills, analyzes the possibility of AOM occurrence in different zones of landfills, discusses its potential applications, and explores the challenges and future research directions for AOM in landfill management. The identification of research gaps and future directions outlined in this review encourages further investigation and advancement in the field of AOM, paving the way for more effective waste stabilization, greenhouse gas reduction, and pollutant mitigation strategies in landfills.

A review on the effectiveness of nanocomposites for the treatment and recovery of oil spill

The introduction of unintended oil spills into the marine ecosystem has a significant impact on aquatic life and raises important environmental concerns. The present review summarizes the recent studies where nanocomposites are applied to treat oil spills. The review deals with the techniques used to fabricate nanocomposites and identify the characteristics of nanocomposites beneficial for efficient recovery and treatment of oil spills. It classifies the nanocomposites into four categories, namely bio-based materials, polymeric materials, inorganic-inorganic nanocomposites, and carbon-based nanocomposites, and provides an insight into understanding the interactions of these nanocomposites with different types of oils. Among nanocomposites, bio-based nanocomposites are the most cost-effective and environmentally friendly. The grafting or modification of magnetic nanoparticles with polymers or other organic materials is preferred to avoid oxidation in wet conditions. The method of synthesizing magnetic nanocomposites and functionalization polymer is essential as it influences saturation magnetization. Notably, the inorganic polymer-based nanocomposite is very less developed and studied for oil spill treatment. Also, the review covers some practical considerations for treating oil spills with nanocomposites. Finally, some aspects of future developments are discussed. The terms "Environmentally friendly," "cost-effective," and "low cost" are often used, but most of the studies lack a critical analysis of the cost and environmental damage caused by chemical alteration techniques. However, the oil and gas industry will considerably benefit from the stimulation of ideas and scientific discoveries in this field.

Spatial green growth in China: exploring the positive role of investment in the treatment of industrial pollution

The industrial sector of China is critical to the country's economic growth. On the other side, industrialisation has resulted in a high rate of emissions, pushing China to spend extensively on industrial pollution remediation. As a result, this study looks at the relationship between investment completed in the treatment of industrial pollution and economic development. Initially, the study used the global Moran's I test (Queen's contiguity matrix) to find spatial autocorrelation for the 'investment completed in the treatment of industrial pollution' factor, where the study found a positive association across Chinese provinces, and suggest the existence of spatial autocorrelation. Thereafter, a time-fixed effect spatial error model was used due to the lowest Akaike information criterion and Bayesian information criterion to analyse regional data of China from 1999 to 2018. The data reveal a positive association between investment completed in the treatment of industrial pollution and regional economic growth, both in the short and long term. Furthermore, the negative consequences of urban wages and foreign investment on investment completed in the treatment of industrial pollution are having the reverse effect on regional green development, necessitating ecologically friendly actions to mitigate the negative environmental effects of both. The results highlight the need for policymakers in other countries to review their plans for economic expansion and create environmentally friendly legislation. By implementing the Chinese green economic growth model, policymakers in industrially polluting nations can reduce industrial pollution and foster green growth in their nation.

Biogenic synthesis of reduced graphene oxide decorated with silver nanoparticles (rGO/Ag NPs) using table olive (olea europaea) for efficient and rapid catalytic reduction of organic pollutants

In this work, graphene oxide (GO) sheets were prepared via a facile electrochemical exfoliation of graphite in acidic medium and subsequent oxidation with potassium permanganate. The GO sheets were employed for preparation of reduced GO adorned with nanosized silver (rGO/Ag NPs) using green reduction of GO and Ag(I) via olive fruit extract as a reducing and immobilizing agent. The crystal phase, morphology, and nanostructure of the prepared catalyst were characterized by XRD, SEM, EDX, UV-Vis and Raman spectroscopy techniques. The as-prepared rGO/Ag NPs showed superior catalytic performance towards the complete reduction (up to 99%) of 4-nitrophenol (4-NPH) to 4-aminophenol (4-APH) and rhodamine B (RhB) to Leuco RhB within 180 s using NaBH₄ at ambient condition. The rate constant (k) values were found to be 0.021 and 0.022 s^-1 for 4-NPH and RhB reduction, respectively. In addition, the regenerated catalyst could be reused after seven cycles without losing any apparent catalytic efficiency. Accounting for the excellent catalytic capability, chemical stability and environment-friendly synthesis protocol, the rGO/Ag NPs has great potential working as a heterogeneous catalyst in the transforming harmful organic contaminants into less harmful or harmless compounds.

Efficient Catalytic Combustion of Cyclohexane over PdAg/Fe2O3 Catalysts under Low-Temperature Conditions: Establishing the Degradation Mechanism Using PTR-TOF-MS and in Situ DRIFTS

Cyclohexane, a typical volatile organic compound (VOC), poses high risks to the environment and humans. Herein, synthesized PdAg/Fe₂O₃ catalysts exhibited exceptional catalytic performance for cyclohexane combustion at lower temperatures (50% mineralization temperature (T₅₀) of 199 °C, 90% mineralization temperature (T₉₀) of 315 °C) than Pd/Fe₂O₃ (T₅₀ of 262 °C, T₉₀ of 335 °C) and Fe₂O₃ (T₅₀ of 305 °C, T₉₀ of 360 °C). In addition, PdAg/Fe₂O₃ displayed enhanced stability by alloying Ag with Pd. The redox and acidity of the PdAg/Fe₂O₃ were studied by XPS, H₂-TPR, and NH₃-TPD. In situ diffuse reflectance infrared Fourier transform spectroscopy and proton-transfer-reaction time-of-flight mass spectrometry were applied to identify the intermediates formed on the catalyst surface and in the tail gas during oxidation, respectively. Results suggested that loading PdAg onto Fe₂O₃ significantly enhanced the adsorption and activation of oxygen and cyclohexane, oxidative dehydrogenation of cyclohexane to benzene, and catalytic cracking of cyclohexane to olefins at low temperatures. This in-depth study will benefit the design and application of efficient catalysts for the effective combustion of VOCs at low temperatures.

Application of sulfur-coated magnetic carbon nanotubes for extraction of some polycyclic aromatic hydrocarbons from water resources

In the present work, novel sulfur-coated magnetic carbon nanotubes (MCNTs-S) material was fabricated by S coating on the MCNTs using a simple heating procedure. TGA, EDX, XRD, TEM, and VSM were employed to characterize the as-prepared composite. Using HPLC-UV system, the produced superparamagnetic sorbent was employed for the extraction and measurement of trace levels of five polycyclic aromatic hydrocarbons (PAHs) in environmental waters. The synergistic effect of the sulfur layer and CNTs substrate is primarily responsible for the remarkable extraction efficiency of the MCNTs-S sorbent towards PAHs. The experimental factors including MCNTs-S dosage, sorption time, elution solvent, ionic strength and solution pH were explored and optimized. Considering that the ionic strength and pH do not have any impact on the PAHs extraction, as a result, there is no need the unnecessary adjustment of the water samples. The linear dynamic ranges and detection limits under optimal conditions were in the range of 0.05-0.11 ng mL^-1 and 0.2-150 ng mL^-1, respectively. The analysis of PAHs in the real samples (sea water and river water) using this approach was successfully assessed with appropriate recovery values (94.6%-99.0%).

Improved feed forward with bald eagle search for conjunctive water management in deficit region

Due to increasing requirements on water resources and a lower recharge rate, the farming seasons are a vital season for the management of groundwater and surface water resource management. This condition necessitates the use of combined water distribution to meet the full water requirements. Analysis of existing surface water resources and related restrictions, this research suggested an algorithm for aquifer stabilization and fulfilling optimum water requirements. To manage the optimum withdrawals and the subsequent drop, this technique first employed the MODFLOW model for simulating the water levels. Next, an improved feed-forward neural network (IFFNN) was combined with an optimization method to create a machine learning (ML) framework. During the last phase, the findings of the optimized connectives approach as well as the relevant fields technologies to determine using improved bald eagle search with least square SVM(IBES-LSSVM) method that predicted the level of water deficit for every period, especially during farming seasons. This approach is based on an improved bald eagle search (IBES) optimization technique for finding the best settings for a least-squares support vector machine (LSSVM). The findings revealed that between 2005 and 2020, the year with the biggest water deficit was 2018 when only roughly 64 percent of water need was satisfied by groundwater (69 percent) and surface water (64 percent) (33 percent). The water depth may have risen by around 0.7 m during the study period if the optimum model had been used. The outcome of this research will help the management forecast future water shortages and make smarter water strategic choices.

Improved neural network with least square support vector machine for wastewater treatment process

This research offers a unique interval by using the predicting approach for discharge indicators of water quality data such as biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N). This is considered one of the significant quality metrics in wastewater treatment plants for water quality management as well as surveillance. To begin, the effluent information for BOD/NH3-N and their supplementary parameters are gathered. Hence BOD and NH3 are considered major feature sources for estimating water pollutants. BOD is high then oxygen level is very low in the water due to pollutants or algae. Ammonia nitrogen is an organic waste component in water from sewage. The significant characteristics with good correlation levels of BOD and NH3-N are examined and identified using a grey correlation analysis method after certain basic data pre-processing procedures. The BOD/NH3-N effluent information of a water treatment plant is predicted using an upgraded feed-forward neural network with the least square support vector machine (FFNN-LSSVM) method. An optimization approach for an enhanced feed-forward neural network (IFFNN) is built by Machine Learning Algorithms. The IFFNN used regular influent water quality, influent rate of flow, and Wastewater performance monitoring and operational conditions as input parameters. For future prediction, input variables were previous different wastewater quality measurements. Lastly, the analysis shows that, when compared to other current algorithms, the proposed methodology can forecast wastewater quality of water with high accuracy in predicting BOD and NH3 levels, limited computation duration, mean error less than 10% and R² is 90% proves better than existing techniques.

Risk assessment of organophosphorus pesticide residues in drinking water resources: Statistical and Monte-Carlo approach

A large part of the world's agricultural production, despite its adverse effects on human health and water resources, depends on the use of pesticides. Despite strict regulations, the use of pesticides continues around the world. This study aimed to determine the residual concentrations of malathion and diazinon in samples of drinking water resources. To achieve this goal, 384 samples from 8 various sites from January to December 2020 were analyzed using gas chromatography (GC) with an electron capture detector (ECD) and liquid-liquid extraction technique. Besides, statistical analysis and a risk-modeling approach supported by an automatic Monte-Carlo procedure were applied. The results showed that there is a high carcinogenic risk regarding malathion and that the low age population is at the most non-carcinogenic risk regarding diazinon.

Use of Parthenium hysterophorus with synthetic chelator for enhanced uptake of cadmium and lead from contaminated soils-a step toward better public health

Parthenium hysterophorus L. is a vigorous plant species with cosmopolitan distribution. It can uptake considerable quantities of heavy metals from the soil and accrue these metals in its different tissue. The use of chelating agent i.e., Ethylenediaminetetraacetic acid (EDTA) can boost up metal uptake capacity. Pot experiment was performed to evaluate phytoextraction potential of P. hysterophorus for lead (Pb) and cadmium (Cd) with and without the aid of EDTA chelator. Shoot length, weight of root and shoot (both fresh and dry), leaves number, and chlorophyll contents of P. hysterophorus got reduced with an increase in metal uptake. The results revealed the highest concentration of Cd in shoot without and with EDTA was 283.6 and 300.1 mg kg^-1, correspondingly. Increase in Pb concentration was also boosted up by the EDTA from its maximum concentration in shoot 4.30-9.56 mg kg^-1. Generally, Pb and Cd concentrations were greater in shoots of P. hysterophorus than the roots regardless of EDTA in the treatments. EDTA also impacted positively the accumulation of essential ions K⁺, Na⁺, and Ca⁺² in P. hysterophorus. The capacity of P. hysterophorus to accumulate Pb and Cd found to be increased with EDTA in the soil. Bringing metals level in the soil in accordance to the WHO standards can improve the ecosystem as well as public health.

The Influence of Taxation on Income Inequality: Analysis of the Practice in the EU Countries

The growing economic inequality around the world is recognized as a global problem of mankind. At the same time, the key tool for reducing inequality and ensuring the achievement of sustainable development goals is the taxation system given its distributive function. That is why this paper puts forward and proves a scientific hypothesis according to which direct taxation has a significant impact on economic inequality, with its scale and sphere depending on the level of economic development and the specific architecture of the tax system adopted in a particular country. The study relies on data from 28 European Union countries, including the United Kingdom, whose tax systems are not identical but harmonized in accordance with European Union directives, the same as the legislation in other economic sectors. Accordingly, it can be concluded that similar institutional characteristics are present. We have used the method of two-stage cluster analysis, which is meant for identifying the natural splitting of the mass of data into groups, then carried out regression analysis and built some models. The contribution of the study is revealing a number of important regularities that are significant for characterizing the dependence of income inequality on direct taxation as well as formulation recommendations for improving the tax policies of European Union countries, with the potential of policy implications. The results obtained can play a significant role in the development and further harmonization of tax systems and resolving the global problem of increased inequality within and between countries.